{"id":"W3200826440","doi":"10.1002/adfm.202104195","title":"Applied Machine Learning for Developing Next‐Generation Functional Materials","year":2021,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Key (lock); Data science; Nanotechnology; Perspective (graphical); Range (aeronautics); Systems engineering; Materials science; Biochemical engineering; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001815097,0.0007099542,0.0006128831,0.001056423,0.0003256062,0.001442251,0.001084269,0.00119893,0.005063983],"category_scores_gemma":[0.0018917,0.000273776,0.0007377645,0.0009953215,0.0006672133,0.001880379,0.001121707,0.00166225,0.001622093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087402,"about_ca_system_score_gemma":0.0007648953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000425815,"about_ca_topic_score_gemma":0.0005741858,"domain_scores_codex":[0.9996345,0.0001323953,0.00002146054,0.00006158403,0.0001162952,0.0000337687],"domain_scores_gemma":[0.9993904,0.0003082398,0.0000600892,0.00008483744,0.0001286489,0.00002780308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001013727,0.0002748004,0.001534468,0.001748429,0.0001655335,0.000130172,0.00006971761,0.2332466,0.03168044,0.2733698,0.01440611,0.4432726],"study_design_scores_gemma":[0.00002006791,0.0001243446,0.0003088722,0.0001917339,0.00002610247,0.0000474481,0.00003330276,0.7910482,0.01819208,0.1395325,0.05044585,0.00002952927],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03156185,0.03261058,0.8944951,0.006574749,0.0008055976,0.0002371997,0.0007011138,0.002135453,0.03087837],"genre_scores_gemma":[0.3266262,0.02200131,0.640802,0.001016195,0.0004392891,0.0006248635,0.001100221,0.0003654844,0.007024458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005063983,"threshold_uncertainty_score":0.01694071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04972178018947698,"score_gpt":0.2698919195457412,"score_spread":0.2201701393562642,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}